Single chip digital signal processing devices (DSP) are relatively well known. DSPs generally are distinguished from general purpose microprocessors in that DSPs typically support accelerated arithmetic operations by including a dedicated multiplier and accumulator (MAC) for performing multiplication of digital numbers. The instruction set for a typical DSP device usually includes a MAC instruction for performing multiplication of new operands and addition with a prior accumulated value stored within an accumulator register. A MAC instruction is typically the only instruction provided in prior art digital signal processors where two DSP operations, multiply followed by add, are performed by the execution of one instruction. However, when performing signal processing functions on data it is often desirable to perform other DSP operations in varying combinations.
An area where DSPs may be utilized is in telecommunication systems. One use of DSPs in telecommunication systems is digital filtering. In this case a DSP is typically programmed with instructions to implement some filter function in the digital or time domain. The mathematical algorithm for a typical finite impulse response (FIR) filter may look like the equation Yn=h0X0+h1X1+h2X2+ . . . +hNXN where hn are fixed filter coefficients numbering from 1 to N and Xn are the data samples. The equation Yn may be evaluated by using a software program. However in some applications, it is necessary that the equation be evaluated as fast as possible. One way to do this is to perform the computations using hardware components such as a DSP device programmed to compute the equation Yn. In order to further speed the process, it is desirable to vectorize the equation and distribute the computation amongst multiple DSPs such that the final result is obtained more quickly. The multiple DSPs operate in parallel to speed the computation process. In this case, the multiplication of terms is spread across the multipliers of the DSPs equally for simultaneous computations of terms. The adding of terms is similarly spread equally across the adders of the DSPs for simultaneous computations. In vectorized processing, the order of processing terms is unimportant since the combination is associative. If the processing order of the terms is altered, it has no effect on the final result expected in a vectorized processing of a function.
In typical micro processors, a MAC operation would require a multiply instruction and an add instruction to perform both multiplication and addition. To perform these two instructions would require two processing cycles. Additionally, a program written for the typical micro processor would require a larger program memory in order to store the extra instructions necessary to perform the MAC operation. In prior art DSP devices, if a DSP operation other than a MAC DSP instruction need be performed, the operation requires separate arithmetic instructions programmed into program memory. These separate arithmetic instructions in prior art DSPs similarly require increased program memory space and processing cycles to perform the operation when compared to a single MAC instruction. It is desirable to reduce the number of processing cycles when performing DSP operations. It is desirable to reduce program memory requirements as well.
DSPs are often programmed in a loop to continuously perform accelerated arithmetic functions including a MAC instruction using different operands. Often times, multiple arithmetic instructions are programmed in a loop to operate on the same data set. The same arithmetic instruction is often executed over and over in a loop using different operands. Additionally, each time one instruction is completed, another instruction is fetched from the program stored in memory during a fetch cycle. Fetch cycles require one or more cycle times to access a memory before instruction execution occurs. Because circuits change state during a fetch cycle, power is consumed and thus it is desirable to reduce the number of fetch cycles. Typically, approximately twenty percent of power consumption may be utilized in the set up and clean up operations of a loop in order to execute DSP instructions. Typically, the loop execution where signal processing of data is performed consumes approximately eighty percent of power consumption with a significant portion being due to instruction fetching. Additionally, because data sets that a DSP device process are usually large, it is also desirable to speed instruction execution by avoiding frequent fetch cycles to memory.
Additionally, the quality of service over a telephone system often relates to the processing speed of signals. That is particularly the case when a DSP is to provide voice processing, such as voice compression, voice decompression, and echo cancellation for multiple channels. More recently, processing speed has become even more important because of the desire to transmit voice aggregated with data in a packetized form for communication over packetized networks. Delays in processing the packetized voice signal tend to result in the degradation of signal quality on receiving ends.
It is desirable to provide improved processing of voice and data signals to enhance the quality of voice and data communication over packetized networks. It is desirable to improve the efficiency of using computing resources when performing signal processing functions.